Big-picture patterns of DNA packaging, gene activation and mutations could hold the information needed to develop a blood-based biopsy for small cell lung cancer patients, according to new work published by scientists at Fred Hutch Cancer Center in the journal Science Advances.
The multidisciplinary team showed that they could use cell-free tumor DNA in blood samples to distinguish between SCLC and non-small cell lung cancer, as well as different subtypes of SCLC, using innovative computational methods that reveal patterns in the activation status of hundreds to thousands of genes.
“There is a deep need for blood-based assays that define subtypes in small cell lung cancer,” said Fred Hutch SCLC researcher David MacPherson, PhD, who co-led the work with Fred Hutch computational biologist Gavin Ha, PhD.
Much of tumor behavior is governed by which genes are turned on, or transcribed, regardless of whether they are mutated. Scientists have defined several signature patterns of gene activation in SCLC, and these subtypes may respond differently to treatment and harbor different treatment vulnerabilities. Patients need tests that can monitor their disease, detect when it transforms into a different lung cancer type, and identify potential treatment targets even when standard biopsies aren’t an option.
The new methods are a step toward such assays, MacPherson said.
Most clinical circulating tumor DNA assays focus on changes to DNA sequences, but the Fred Hutch team built an assay that reveals gene activity and regulation status in tumors using a snippet of cell-free DNA.
“Our approach demonstrates that a full-featured circulating tumor DNA assay has the potential to classify clinical subtypes driven by transcriptional programs,” Ha said.
This approach is especially important for SCLC and other tumors without DNA sequence information that can inform treatment decisions, he said.
“This assay expands the boundaries for potentially using circulating tumor DNA to improve treatment selection and cancer management,” Ha said.
SCLC is an aggressive disease that is prone to metastasize, or spread. About 238,340 people were diagnosed with lung cancer in the U.S. in 2023, and about 14% of them had small cell lung cancer. Most patients are diagnosed with late-stage disease, and the percent of people who are alive five years after diagnosis can be as low as 6%.
Most cases respond well to chemotherapy, but the disease almost always recurs. In recent years, doctors have extended patients' lifespan by adding immune checkpoint inhibitors, a type of immunotherapy, but they’re not cures. Researchers like MacPherson are working to develop SCLC-targeted therapies, but it’s not easy.
“For a lot of cancer types, the distinct ‘flavors’ of the cancer types are driven by gene mutations,” MacPherson said.
In NSCLC, a mutation in the EGFR gene drives some tumors and can be targeted with a specific inhibitor.
“In SCLC, we don’t really have that,” he said. Instead, in each “flavor” or subtype, a key protein orchestrates a distinct large-scale gene expression program. This makes it hard to pinpoint individual targets with therapeutic potential.
These different gene expression patterns already have important implications for patients. For example, one SCLC subtype is linked to better responses to immunotherapy, which doesn’t work for every patient. And sometimes patients diagnosed with NSCLC find that their tumors evade treatment by evolving into SCLC.
“There's a deep need for blood-based assays to define these subtypes in small cell lung cancer patients,” MacPherson said.
Such an assay could help oncologists tailor SCLC treatment when new, targeted strategies reach the clinic. They could also help doctors monitor patients for recurrence or detect when a patient’s disease has switched from NSCLC to SCLC, which will change their prognosis and will dictate a new treatment strategy.
Liquid biopsies are gaining attention as potentially quicker, less invasive and cheaper alternatives to standard biopsies. SCLC tumors release a lot of DNA into the blood, which could be used as biopsy material, MacPherson said.
Fred Hutch file photos
But developing blood-based cell-free DNA tests is tricky. First, cell-free DNA doesn’t float in long, easy-to-sequence strings. It’s made up of small snippets that must be reassembled like a multimillion-piece jigsaw puzzle. Secondly, most of the DNA in our blood is from healthy cells, even in patients with large tumors. To top it off, most of the DNA in cancer cells is the same as the DNA from healthy cells.
To parse out the tumor DNA and discover its important information, Joe Hiatt, MD, PhD, a research associate in MacPherson’s lab teamed up with Anna-Lisa Doebley, PhD, while she was an MD/PhD student in Ha’s lab. (Doebley conducted the research portion of her graduate work with Ha and is currently finishing her MD at the University of Washington School of Medicine.) Ha and his group specialize in developing computational approaches to re-assemble the DNA jigsaw and find informative patterns in circulating tumor DNA.
Ha doesn’t focus on mutations in individual genes. Instead, he looks at larger patterns, particularly of DNA packaging, which can tell him about which genes are turned on (transcribed) and turned off (silenced).
The most basic unit of DNA packaging is a wheel-shaped protein complex called the nucleosome. DNA strands wrap around nucleosomes like yarn around a spool. The more nucleosomes in a section of DNA, the tighter the packaging and the more “silent” the gene region is. Looser areas where genes are turned on have fewer nucleosomes.
When DNA is released by cells (both cancerous and healthy) into the blood, nucleosomes protect it. The snippets of DNA floating in our blood reflect the regions with more protective nucleosomes. Ha has developed methods, called nucleosome profiling, to glean gene expression patterns from nucleosome-protected snippets of DNA in blood.
So Doebley and Hiatt decided to develop a targeted strategy to discover them. The team looked at regions of DNA called transcription start sites, or TSSs, where the “reading” of genes starts.
Transcription is orchestrated by transcription factors, a wide-ranging group of proteins that help turn genes on and off. The team suspected that the different genetic programs used by the different SCLC subtypes would be reflected in different patterns of DNA packaging at these genes’ start sites — and that these patterns could be found in circulating tumor DNA.
To give themselves a leg up, Hiatt and Doebley started with preclinical models in which cell-free tumor DNA is easier to sift out from DNA released by healthy cells: patient-derived xenograft models, or PDXs.
In PDX models, tumor tissue taken from patients is implanted into mice. This means that all the tumor DNA circulating in the blood of a PDX mouse is of human origin.
The team drew from eight NSCLC PDX models and 20 SCLC PDX models (including one from a patient whose tumor had transformed from NSCLC to SCLC) to draw from. The team had detailed molecular information about each tumor.
“They had ground truth about activation of transcription factors, expression of all genes, and how we can correlate that with the TSS [transcription start site] signal,” MacPherson said.
They created a focused sequencing strategy to detect DNA from relevant regions, and Doebley built her probabilistic models by tailoring Ha’s team’s nucleosome profiling methods to this targeted panel. She formulated probabilistic models that account for the fact that the amount of tumor DNA in blood plasma can vary, and which accurately estimate the fraction of each SCLC subtype even when in samples with 5% tumor DNA.
Doebley and Ha built one model to predict likelihood that cell-free DNA came from an NSCLC or an SCLC tumor. The second model distinguished different SCLC subtypes.
All told, the predictive models examined more than 13,000 transcription start sites and more than 1,000 transcription factor binding sites. The assays captured the sequences of nearly 850 genes.
Doebley and Hiatt then tested the models against cell-free DNA in patient samples to see how they fared in a more clinically relevant context.
They found that their computational model was very good at predicting whether DNA had come from an NSCLC or SCLC tumor, suggesting that their approach has potential for detecting when a patient’s tumor transforms from NSCLC to SCLC, MacPherson said.
The model that predicted SCLC subtype performed well, but was hampered by the limited selection of subtypes in the patient samples. Certain subtypes were well represented, but one SCLC subtype was not included in the cohort.
Though preliminary, the results show that liquid biopsies based on large-scale patterns in DNA packaging have potential as tools to monitor SCLC, MacPherson said.
The team is working toward further refining and validating their models to improve and expand their predictive capabilities. They will likely be able to winnow down the key TSSs, transcription factor binding sites and mutations to a smaller, but equally informative panel.
“It was remarkable to us that only a smaller set of informative genomic regions was needed for our computational models,” Ha said. “This has implications for more cost-effective and easier translation into the clinic.”
The refined panel will be tested against larger, more comprehensive sets of patient samples, including more from patients whose tumors change from NSCLC to SCLC.
“The other future direction is to broaden the types of phenotypes that we want to try to capture with this assay,” MacPherson said.
This would allow them to do more than merely assign patient SCLC tumors to particular subsets, he said. The team may be able to use the patterns in cell-free tumor DNA to qualities that oncologists could use to someday direct a patient’s treatment, like targets for therapies like antibody-drug conjugates or genetically engineered immune cells.
The investigators also want to link the patterns they’ve detected to clinical responses, which will also help tailor treatment regimens in the future.
The approach has implications for other tumor types as well, Ha and MacPherson noted.
“This was the first study to comprehensively assay thousands of [gene transcription] start sites. An important conclusion from our paper is that cell-free DNA contains information about the activation of a lot of these sites,” MacPherson said.
This information will likely be as important and informative for other tumors as it is for SCLC.
In the short term, MacPherson envisions similar assays being used to improve clinical trials, helping identify patients who are the best candidate for a new therapy, or giving researchers information about why certain patients respond and others do not. He’s also interested in discovering whether a similar assay could be used to detect at the molecular level tumors that are responding positively to treatment even if the response may not yet be clinically apparent.
“A clinical assay is our ultimate goal, and the next steps of our research directions are focused on that,” MacPherson said.
This work was supported by the National Institutes of Health, the Kuni Foundation and the Conquer Cancer Foundation.
Sabrina Richards, a staff writer at Fred Hutchinson Cancer Center, has written about scientific research and the environment for The Scientist and OnEarth Magazine. She has a PhD in immunology from the University of Washington, an MA in journalism and an advanced certificate from the Science, Health and Environmental Reporting Program at New York University. Reach her at srichar2@fredhutch.org.
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